用扩散模型生成LUT，让视频自动匹配参考画面的美术风格。
Video Color Grading via Look-Up Table Generation
- 通过扩散模型生成LUT，实现参考画面与输入视频的颜色属性对齐。
- 保持视频结构细节完整，推理速度快，支持端到端艺术化调色。
- 支持文本提示调节对比度、亮度等低层特征，适配用户偏好。
与色彩校正和迁移不同，色彩分级是为视频添加艺术或叙事效果以营造特定视觉风格或情绪，通常依赖专业调色师。本文提出一种基于参考图像的视频色彩分级框架，核心思想是利用扩散模型显式生成用于颜色属性对齐的查找表（LUT）。训练目标为确保参考场景的高层特征（如风格、氛围、情感）与输入视频一致。该基于LUT的方法可在不损失整帧结构细节的前提下实现快速推理。此外，我们构建了融合文本提示的流程，用于增强对比度、亮度等低层特征。实验结果，包括广泛的用户研究，验证了方法的有效性。代码已公开于 https://github.com/seunghyuns98/VideoColorGrading。
原文摘要 · Abstract (English)
Different from color correction and transfer, color grading involves adjusting colors for artistic or storytelling purposes in a video, which is used to establish a specific look or mood. However, due to the complexity of the process and the need for specialized editing skills, video color grading remains primarily the domain of professional colorists. In this paper, we present a reference-based video color grading framework. Our key idea is explicitly generating a look-up table (LUT) for color attribute alignment between reference scenes and input video via a diffusion model. As a training objective, we enforce that high-level features of the reference scenes like look, mood, and emotion should be similar to that of the input video. Our LUT-based approach allows for color grading without any loss of structural details in the whole video frames as well as achieving fast inference. We further build a pipeline to incorporate a user-preference via text prompts for low-level feature enhancement such as contrast and brightness, etc. Experimental results, including extensive user studies, demonstrate the effectiveness of our approach for video color grading. Codes are publicly available at https://github.com/seunghyuns98/VideoColorGrading.
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